Modeling optical gap of cupric oxide nanomaterial semiconductor using hybrid intelligent method

Abstract Copper II oxide (CuO) semiconductor belongs to the compound of metal oxide with abundant uniqueness and features which facilitate its wider applicability. The nature of the optical band gap of this semiconductor strengthens its usage for many technological and industrial applications while chemical doping mechanisms through breaking of symmetry of the host semiconductor have proven successful for its energy gap tuning for meeting the desired demand. This work proposes hybrid particle swarm optimization-based support vector regression (PBSVR) as an effective intelligent algorithm for determining optical band gap using lattice parameters (distorted) as input predictors. The developed PBSVR model demonstrates low mean absolute error (MAE) of 0.287 eV, low root mean square error (RMSE) of 0.367 eV and high correlation coefficient (CC) of 90.3 % while validating on testing samples. PBSVR model performs better than three existing models in the literature which include stepwise regression model (SWR), extreme learning machine model with sigmoid function (ELM-IP-Sig) and sine function (ELM-IP-Sine). On the basis of MAE, the developed PBSVR model outperforms ELM-IP-Sig, ELM-IP-Sine and SWR models with performance improvement of 33.7%, 26.93% and 67.6%, respectively. The PBSVR model further investigates the influence of iron and aluminum on the semiconductor energy gap while the predicted optical band gaps agree excellently with the experimental optical gaps. The experimental stress circumvention potentials of the developed PBSVR model coupled with its superior performance over the existing models are of great importance in ensuring precise and quick characterization of CuO optical gap for desired applications.